Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

1. An apparatus, comprising: a first module of at least three modules, wherein the first module configured to generate class independent region proposals to provide a region; a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein the second module comprises a five convolutional layers with three fully connected layers, a network configured to fuse the three fully connected layers, separate networks for face detection, landmark detection, visibility determination, pose estimation, and gender determination; and a third module of the at least three modules is configured to perform post-processing on the classified region.

2. The apparatus of claim 1 , wherein the third module comprises at least one of an iterative region proposal or landmark-based non-maximum suppression.

3. An apparatus, comprising: at least one processor; and at least one memory including computer program instructions, wherein the at least one memory and the computer program instructions are configured to select a set of data for facial analysis; and apply the set of data to a network comprising at least three modules, wherein a first module of the at least three modules is configured to generate class independent region proposals to provide a region, wherein a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein the second module comprises a five convolutional layers with three fully connected layers, a network configured to fuse the three fully connected layers, and separate networks for face detection, landmark detection, visibility determination, pose estimation, and gender determination; and wherein a third module of the at least three modules is configured to perform post-processing on the classified region.

4. The apparatus of claim 3 , wherein the third module comprises at least one of an iterative region proposal or landmark-based non-maximum suppression.

5. A method, comprising: selecting a set of data for facial analysis; and applying the set of data to a network comprising at least three modules, wherein a first module of the at least three modules is configured to generate class independent region proposals to provide a region, wherein a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein the second module comprises a five convolutional layers with three fully connected layers, a network configured to fuse the three fully connected layers, and separate networks for face detection, landmark detection, visibility determination, pose estimation, and gender determination, and wherein a third module of the at least three modules is configured to perform post-processing on the classified region.

6. The method of claim 5 , wherein the third module comprises at least one of an iterative region proposal or landmark-based non-maximum suppression.

7. An apparatus, comprising: means for selecting a set of data for facial analysis; and means for applying the set of data to a network comprising at least three modules, wherein a first module of the at least three modules is configured to generate class independent region proposals to provide a region, wherein a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein the second module comprises a five convolutional layers with three fully connected layers, a network configured to fuse the three fully connected layers, and separate networks for face detection, landmark detection, visibility determination, pose estimation, and gender determination, and wherein a third module of the at least three modules is configured to perform post-processing on the classified region.

8. The apparatus of claim 7 , wherein the third module comprises at least one of an iterative region proposal or landmark-based non-maximum suppression.